Evolutionary Optimization Through PAC Learning
Forbes J. Burkowski Department of Computer Science, University of Waterloo, Canada
Abstract
Strategies for evolutionary optimization (EO) typically experience a convergence phenomenon that involves a steady increase in the frequency of particular allelic combinations. When some allele is consistent throughout the population we essentially have a reduction in the dimension of the binary string space that is the objective function domain of feasible solutions. In this paper we consider dimension reduction to be the most salient feature of evolutionary optimization and present the theoretical setting for a novel algorithm that manages this reduction in a very controlled albeit stochastic manner. The ...
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